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Merge remote-tracking branch 'upstream/3.4' into merge-3.4

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This commit is contained in:
Alexander Alekhin 2021-10-08 11:05:45 +00:00
commit cca4c47781
12 changed files with 192 additions and 158 deletions
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2
cmake/OpenCVDetectCXXCompiler.cmake
View File

@ -171,6 +171,8 @@ elseif(MSVC)
set(OpenCV_RUNTIME vc15)
elseif(MSVC_VERSION MATCHES "^192[0-9]$")
set(OpenCV_RUNTIME vc16)
elseif(MSVC_VERSION MATCHES "^193[0-9]$")
set(OpenCV_RUNTIME vc17)
else()
message(WARNING "OpenCV does not recognize MSVC_VERSION \"${MSVC_VERSION}\". Cannot set OpenCV_RUNTIME")
endif()

14
cmake/templates/OpenCVConfig.root-WIN32.cmake.in
View File

@ -137,6 +137,20 @@ elseif(MSVC)
set(OpenCV_RUNTIME vc14) # selecting previous compatible runtime version
endif()
endif()
elseif(MSVC_VERSION MATCHES "^193[0-9]$")
set(OpenCV_RUNTIME vc17)
check_one_config(has_VS2022)
if(NOT has_VS2022)
set(OpenCV_RUNTIME vc16)
check_one_config(has_VS2019)
if(NOT has_VS2019)
set(OpenCV_RUNTIME vc15) # selecting previous compatible runtime version
check_one_config(has_VS2017)
if(NOT has_VS2017)
set(OpenCV_RUNTIME vc14) # selecting previous compatible runtime version
endif()
endif()
endif()
endif()
elseif(MINGW)
set(OpenCV_RUNTIME mingw)

4
modules/core/src/parallel.cpp
View File

@ -57,8 +57,8 @@
#endif
#if defined __unix__ || defined __APPLE__ || defined __GLIBC__ \
|| defined __HAIKU__ || defined __EMSCRIPTEN__ || defined __FreeBSD__ \
|| defined __OpenBSD__
|| defined __HAIKU__ || defined __EMSCRIPTEN__ \
|| defined __FreeBSD__ || defined __NetBSD__ || defined __OpenBSD__
#include <unistd.h>
#include <stdio.h>
#include <sys/types.h>

119
modules/core/src/system.cpp
View File

@ -55,6 +55,18 @@
#include <opencv2/core/utils/filesystem.private.hpp>
#ifndef OPENCV_WITH_THREAD_SANITIZER
#if defined(__clang__) && defined(__has_feature)
#if __has_feature(thread_sanitizer)
#define OPENCV_WITH_THREAD_SANITIZER 1
#include <atomic> // assume C++11
#endif
#endif
#endif
#ifndef OPENCV_WITH_THREAD_SANITIZER
#define OPENCV_WITH_THREAD_SANITIZER 0
#endif
namespace cv {
static void _initSystem()
@ -1383,64 +1395,62 @@ namespace details {
#endif
#endif
template <class T>
class DisposedSingletonMark
{
private:
static bool mark;
protected:
DisposedSingletonMark() {}
~DisposedSingletonMark()
{
mark = true;
}
public:
static bool isDisposed() { return mark; }
};
// TLS platform abstraction layer
class TlsAbstraction : public DisposedSingletonMark<TlsAbstraction>
class TlsAbstraction
{
public:
TlsAbstraction();
~TlsAbstraction();
void* getData() const
~TlsAbstraction()
{
if (isDisposed()) // guard: static initialization order fiasco
return NULL;
return getData_();
}
void setData(void *pData)
{
if (isDisposed()) // guard: static initialization order fiasco
return;
return setData_(pData);
// TlsAbstraction singleton should not be released
// There is no reliable way to avoid problems caused by static initialization order fiasco
// NB: Do NOT use logging here
fprintf(stderr, "OpenCV FATAL: TlsAbstraction::~TlsAbstraction() call is not expected\n");
fflush(stderr);
}
void* getData() const;
void setData(void *pData);
void releaseSystemResources();
private:
void* getData_() const;
void setData_(void *pData);
#ifdef _WIN32
#ifndef WINRT
DWORD tlsKey;
bool disposed;
#endif
#else // _WIN32
pthread_key_t tlsKey;
#if OPENCV_WITH_THREAD_SANITIZER
std::atomic<bool> disposed;
#else
bool disposed;
#endif
#endif
};
template<> bool DisposedSingletonMark<TlsAbstraction>::mark = false;
static TlsAbstraction& getTlsAbstraction_()
class TlsAbstractionReleaseGuard
{
static TlsAbstraction g_tls; // disposed in atexit() handlers (required for unregistering our callbacks)
return g_tls;
}
TlsAbstraction& tls_;
public:
TlsAbstractionReleaseGuard(TlsAbstraction& tls) : tls_(tls)
{
/* nothing */
}
~TlsAbstractionReleaseGuard()
{
tls_.releaseSystemResources();
}
};
// TODO use reference
static TlsAbstraction* getTlsAbstraction()
{
static TlsAbstraction* instance = &getTlsAbstraction_();
return DisposedSingletonMark<TlsAbstraction>::isDisposed() ? NULL : instance;
static TlsAbstraction *g_tls = new TlsAbstraction(); // memory leak is intended here to avoid disposing of TLS container
static TlsAbstractionReleaseGuard g_tlsReleaseGuard(*g_tls);
return g_tls;
}
@ -1448,15 +1458,15 @@ static TlsAbstraction* getTlsAbstraction()
#ifdef WINRT
static __declspec( thread ) void* tlsData = NULL; // using C++11 thread attribute for local thread data
TlsAbstraction::TlsAbstraction() {}
TlsAbstraction::~TlsAbstraction()
void TlsAbstraction::releaseSystemResources()
{
cv::__termination = true; // DllMain is missing in static builds
}
void* TlsAbstraction::getData_() const
void* TlsAbstraction::getData() const
{
return tlsData;
}
void TlsAbstraction::setData_(void *pData)
void TlsAbstraction::setData(void *pData)
{
tlsData = pData;
}
@ -1465,6 +1475,7 @@ void TlsAbstraction::setData_(void *pData)
static void NTAPI opencv_fls_destructor(void* pData);
#endif // CV_USE_FLS
TlsAbstraction::TlsAbstraction()
: disposed(false)
{
#ifndef CV_USE_FLS
tlsKey = TlsAlloc();
@ -1473,9 +1484,10 @@ TlsAbstraction::TlsAbstraction()
#endif // CV_USE_FLS
CV_Assert(tlsKey != TLS_OUT_OF_INDEXES);
}
TlsAbstraction::~TlsAbstraction()
void TlsAbstraction::releaseSystemResources()
{
cv::__termination = true; // DllMain is missing in static builds
disposed = true;
#ifndef CV_USE_FLS
TlsFree(tlsKey);
#else // CV_USE_FLS
@ -1483,16 +1495,20 @@ TlsAbstraction::~TlsAbstraction()
#endif // CV_USE_FLS
tlsKey = TLS_OUT_OF_INDEXES;
}
void* TlsAbstraction::getData_() const
void* TlsAbstraction::getData() const
{
if (disposed)
return NULL;
#ifndef CV_USE_FLS
return TlsGetValue(tlsKey);
#else // CV_USE_FLS
return FlsGetValue(tlsKey);
#endif // CV_USE_FLS
}
void TlsAbstraction::setData_(void *pData)
void TlsAbstraction::setData(void *pData)
{
if (disposed)
return; // no-op
#ifndef CV_USE_FLS
CV_Assert(TlsSetValue(tlsKey, pData) == TRUE);
#else // CV_USE_FLS
@ -1503,12 +1519,14 @@ void TlsAbstraction::setData_(void *pData)
#else // _WIN32
static void opencv_tls_destructor(void* pData);
TlsAbstraction::TlsAbstraction()
: disposed(false)
{
CV_Assert(pthread_key_create(&tlsKey, opencv_tls_destructor) == 0);
}
TlsAbstraction::~TlsAbstraction()
void TlsAbstraction::releaseSystemResources()
{
cv::__termination = true; // DllMain is missing in static builds
disposed = true;
if (pthread_key_delete(tlsKey) != 0)
{
// Don't use logging here
@ -1516,12 +1534,16 @@ TlsAbstraction::~TlsAbstraction()
fflush(stderr);
}
}
void* TlsAbstraction::getData_() const
void* TlsAbstraction::getData() const
{
if (disposed)
return NULL;
return pthread_getspecific(tlsKey);
}
void TlsAbstraction::setData_(void *pData)
void TlsAbstraction::setData(void *pData)
{
if (disposed)
return; // no-op
CV_Assert(pthread_setspecific(tlsKey, pData) == 0);
}
#endif
@ -1549,6 +1571,7 @@ public:
TlsStorage() :
tlsSlotsSize(0)
{
(void)getTlsAbstraction(); // ensure singeton initialization (for correct order of atexit calls)
tlsSlots.reserve(32);
threads.reserve(32);
g_isTlsStorageInitialized = true;
@ -1786,11 +1809,11 @@ static void WINAPI opencv_fls_destructor(void* pData)
#endif // CV_USE_FLS
#endif // _WIN32
static TlsAbstraction* const g_force_initialization_of_TlsAbstraction
static TlsStorage* const g_force_initialization_of_TlsStorage
#if defined __GNUC__
__attribute__((unused))
#endif
= getTlsAbstraction();
= &getTlsStorage();
#else // OPENCV_DISABLE_THREAD_SUPPORT

20
modules/dnn/src/layers/convolution_layer.cpp
View File

@ -48,6 +48,7 @@
#include "../ie_ngraph.hpp"
#include "../op_vkcom.hpp"
#include <opencv2/core/utils/configuration.private.hpp>
#include <opencv2/core/utils/logger.hpp>
#include "opencv2/core/hal/hal.hpp"
@ -1736,7 +1737,26 @@ public:
config.pad = pad;
config.stride = stride;
config.dilation = dilation;
if (inputs[0].dims != 4 && inputs[0].dims != umat_blobs[0].dims)
{
static bool bypassCheck = utils::getConfigurationParameterBool("OPENCV_OCL4DNN_CONVOLUTION_IGNORE_INPUT_DIMS_4_CHECK", false);
if (!bypassCheck)
{
CV_LOG_ERROR(NULL, "DNN/OpenCL: Unsupported configuration: inputs[0].dims=" << inputs[0].dims << " umat_blobs[0].dims=" << umat_blobs[0].dims
<< ". Consider reporting complete reproducer to https://github.com/opencv/opencv/issues/20833."
<< " You can skip this check temporary through OPENCV_OCL4DNN_CONVOLUTION_IGNORE_INPUT_DIMS_4_CHECK=1"
);
return false;
}
}
config.group = inputs[0].size[1] / umat_blobs[0].size[1];
if (config.group < 1) // config.group == 0 causes div by zero in ocl4dnn code
{
CV_LOG_WARNING(NULL, "DNN/OpenCL: Unsupported config.group=" << config.group
<< ". Consider reporting complete reproducer to https://github.com/opencv/opencv/issues/20833"
);
return false;
}
config.bias_term = umat_blobs.size() == 2;
config.use_half = use_half;

2
modules/dnn/src/ocl4dnn/include/ocl4dnn.hpp
View File

@ -222,8 +222,6 @@ class OCL4DNNConvSpatial
bool createDWConvKernel(int32_t blockWidth,
int32_t blockHeight,
int32_t blockDepth);
void CreateSubBuffer(const UMat& buffer, UMat& sub_buffer,
int32_t offset, int32_t size, bool write_only);
bool convolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
int32_t numImages,

101
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
View File

@ -167,6 +167,7 @@ OCL4DNNConvSpatial<Dtype>::OCL4DNNConvSpatial(OCL4DNNConvConfig config)
channels_ = config.in_shape[dims - spatial_dims - 1];
num_output_ = config.out_shape[dims - spatial_dims - 1];
group_ = config.group;
CV_CheckGT(group_, 0, ""); // avoid div by zero below
fused_activ_ = OCL4DNN_CONV_FUSED_ACTIV_NONE;
fused_eltwise_ = false;
@ -218,14 +219,7 @@ OCL4DNNConvSpatial<Dtype>::OCL4DNNConvSpatial(OCL4DNNConvConfig config)
#endif
if (!use_cache_path_)
{
static int warn_ = 0;
if (!warn_)
{
std::cerr
<< "OpenCV(ocl4dnn): Kernel configuration cache directory doesn't exist: " << cache_path_ << std::endl
<< std::endl;
warn_ = true;
}
CV_LOG_ONCE_ERROR(NULL, "OpenCV(ocl4dnn): Kernel configuration cache directory doesn't exist: " << cache_path_);
}
}
@ -418,7 +412,6 @@ void OCL4DNNConvSpatial<Dtype>::setupKernelDetails(int32_t kernelType,
addDef("CHANNELS", channels_ / group_);
addDef("APPLY_BIAS", bias_term_);
addDef("OUTPUT_Z", M_);
addDef("ZPAR", 1);
setFusionDefine(fused_activ_, fused_eltwise_);
src_ = cv::ocl::dnn::conv_layer_spatial_oclsrc;
@ -672,8 +665,7 @@ void interleaveMatrix(Dtype* mem_dst, const Dtype *mem,
int r, int c, int interleavedRows, int nonInterleavedRows,
int blockWidth, int rowAlignment )
{
CHECK_EQ(interleavedRows % 2, 0) <<
"interleaveMatrix only supports even values for interleavedRows.";
CV_Check(interleavedRows, interleavedRows % 2 == 0, "interleaveMatrix only supports even values for interleavedRows.");
size_t memSize = r * c * sizeof(float);
size_t dstSize = memSize *
@ -685,9 +677,12 @@ void interleaveMatrix(Dtype* mem_dst, const Dtype *mem,
const int yStride = c * 2;
const Dtype *pSrc = mem;
Dtype* pDst = mem_dst;
for (int y = 0; y < r;) {
for (int rows = 0; rows < interleavedRows; rows += 2) {
if ( y >= r ) break;
for (int y = 0; y < r;)
{
for (int rows = 0; rows < interleavedRows; rows += 2)
{
if (y >= r)
break;
if ((c % xStride) == 0) {
for (int x = 0; x < c / xStride; x++) {
memcpy(pDst + x * xStride * 2, // NOLINT
@ -712,11 +707,14 @@ void interleaveMatrix(Dtype* mem_dst, const Dtype *mem,
y += 2;
}
for (int rows = 0; rows < nonInterleavedRows; rows++) {
if (y >= r) break;
for (int rows = 0; rows < nonInterleavedRows; rows++)
{
if (y >= r)
break;
const int stride = rowAlignment;
int remaining = c;
for (int x = 0; x < c; x += stride) {
for (int x = 0; x < c; x += stride)
{
if (remaining >= stride) {
memcpy(pDst + x * 2, pSrc + x, stride * sizeof(Dtype)); // NOLINT
remaining -=stride;
@ -852,34 +850,6 @@ bool OCL4DNNConvSpatial<float>::createBasicKernel(int32_t blockWidth,
return false;
}
template<>
void OCL4DNNConvSpatial<float>::CreateSubBuffer(const UMat& buffer, UMat& sub_buffer,
int32_t offset, int32_t size, bool write_only)
{
cl_mem sub_mem;
cl_buffer_region region;
cl_int err;
size_t element_size = (use_half_) ? sizeof(short) : sizeof(float);
region.origin = offset * element_size + buffer.offset;
region.size = size * element_size;
sub_mem = clCreateSubBuffer((cl_mem)buffer.handle(ACCESS_READ),
write_only ? CL_MEM_WRITE_ONLY : CL_MEM_READ_ONLY,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
if (err)
{
std::cout << "Failed to create sub buffer." << std::endl;
return;
}
int step = element_size, rows = size, cols = 1;
ocl::convertFromBuffer(sub_mem, step, rows, cols,
(use_half_) ? CV_16SC1 : CV_32FC1, sub_buffer);
//decrease ocl mem refcount
clReleaseMemObject(sub_mem);
}
template<>
bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
const UMat &weight, const UMat &bias,
@ -938,7 +908,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
kernel.set(argIdx++, (uint16_t)output_h_);
if (!kernel.run_(3, config->global_work_size, config->local_work_size, false))
{
std::cout << "IDLF kernel run failed." << std::endl;
CV_LOG_ERROR(NULL, "DNN/OpenCL: IDLF kernel run failed");
return false;
}
}
@ -1012,7 +982,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
if (!kernel.run_(3, global_size, config->local_work_size, false))
{
std::cout << "GEMM like kernel run failed." << std::endl;
CV_LOG_ERROR(NULL, "DNN/OpenCL: GEMM like kernel run failed");
return false;
}
}
@ -1115,14 +1085,9 @@ float OCL4DNNConvSpatial<float>::timedConvolve(const UMat &bottom, UMat &top,
{
queue = cv::ocl::Queue::getDefault();
}
catch (const cv::Exception&)
catch (const std::exception& e)
{
static int warn_ = 0;
if (!warn_)
{
std::cout << "OpenCV(ocl4dnn): Can't get OpenCL default queue for auto-tuning." << std::endl;
warn_ = true;
}
CV_LOG_ONCE_ERROR(NULL, "OpenCV(ocl4dnn): Can't get OpenCL default queue for auto-tuning: " << e.what());
return 1e6;
}
@ -1326,9 +1291,9 @@ ocl::Program OCL4DNNConvSpatial<Dtype>::compileKernel()
phash.insert(std::pair<std::string, ocl::Program>(kernel_name_, program));
if (!program.ptr())
{
std::cout << "Failed to compile kernel: " << kernel_name_
<< ", buildflags: " << options
<< ", errmsg: " << errmsg << std::endl;
CV_LOG_WARNING(NULL, "DNN/OpenCL: Failed to compile kernel: " << kernel_name_
<< ", buildflags: '" << options << "', errmsg: '" << errmsg << "'"
);
}
return program;
}
@ -1754,7 +1719,8 @@ void OCL4DNNConvSpatial<float>::setupConvolution(const UMat &bottom,
fastestTime = kernelQueue[x]->executionTime;
}
}
if (fastestKernel < 0) break;
if (fastestKernel < 0)
break;
// Test fastest kernel
bool verified = verifyResult(bottom, top, weight, bias, numImages, kernelQueue[fastestKernel], verifyTop);
if (verified == true) {
@ -1913,17 +1879,18 @@ bool OCL4DNNConvSpatial<Dtype>::setupKernelByConfig(int x, int y, int z, int typ
{
if (z == 1)
z = 16;
CHECK_EQ(z == 16 || z == 8, true) << "invalid SIMD size" << std::endl;
CV_Check(z, z == 16 || z == 8, "DNN/OpenCL: IDLF - invalid SIMD size");
}
kernelQueue.clear();
createConvolutionKernel(type, x, y, z);
if (kernelQueue.size() != 1) {
std::cerr << "Failed setup kernel by config:"
if (kernelQueue.size() != 1)
{
CV_LOG_ERROR(NULL, "DNN/OpenCL: Failed setup kernel by config: "
<< " x = " << x
<< " y = " << y
<< " z = " << z
<< " type = " << type
<< std::endl;
);
return false;
}
bestKernelConfig = kernelQueue[0];
@ -1955,13 +1922,9 @@ bool OCL4DNNConvSpatial<Dtype>::loadTunedConfig()
{
if (cache_path_.empty())
{
static int warn_ = 0;
if (!warn_)
{
std::cout << "OpenCV(ocl4dnn): consider to specify kernel configuration cache directory " << std::endl
<< " via OPENCV_OCL4DNN_CONFIG_PATH parameter." << std::endl;
warn_ = true;
}
CV_LOG_ONCE_WARNING(NULL, "OpenCV(ocl4dnn): consider to specify kernel configuration cache directory "
"through OPENCV_OCL4DNN_CONFIG_PATH parameter."
);
}
return false;
}

39
modules/dnn/src/opencl/conv_layer_spatial.cl
View File

@ -161,23 +161,15 @@ __kernel void ConvolveBasic(
const int out_idx = get_global_id(0); // 1D task layout: [output_width * output_height * OUTPUT_Z]
const int plane_size = output_width * output_height;
const int out_plane_idx = out_idx % plane_size;
const int outputZ = out_idx / plane_size;
const int outputZ = out_idx / plane_size; // kernelNum
const int outputY = out_plane_idx / output_width;
const int outputX = out_plane_idx % output_width;
const int kernelNum = outputZ * ZPAR;
if (kernelNum < OUTPUT_Z)
if (outputZ < OUTPUT_Z)
{
Dtype sum[ZPAR];
for (int kern = 0; kern < ZPAR; kern++)
{
sum[kern] = 0.0f;
}
Dtype sum = 0.0f;
const int org_y = outputY * STRIDE_Y - pad_h;
const int org_x = outputX * STRIDE_X - pad_w;
const int currentKernelOffset = kernel_offset + kernelNum*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS;
#if APPLY_BIAS
const int biasIndex = bias_offset + kernelNum;
#endif
const int currentKernelOffset = kernel_offset + outputZ*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS;
const int local_image_offset = org_y * input_width + org_x;
const int imageSize = input_width * input_height;
__global Dtype* image_dataPtr = (image_data + (image_offset + local_image_offset));
@ -186,17 +178,13 @@ __kernel void ConvolveBasic(
{
for (int y = 0; y < KERNEL_HEIGHT; y++)
{
int y_ = org_y + y * DILATION_Y;
for (int x = 0; x < KERNEL_WIDTH; x++)
{
int y_ = org_y + y * DILATION_Y;
int x_ = org_x + x * DILATION_X;
if (!(y_ >= 0 && y_ < input_height && x_ >= 0 && x_ < input_width))
if (y_ >= 0 && y_ < input_height && x_ >= 0 && x_ < input_width)
{
continue;
}
for (int kern = 0; kern < ZPAR; kern++)
{
sum[kern] += image_dataPtr[x * DILATION_X] * kernel_dataPtr[kern*KERNEL_HEIGHT*KERNEL_WIDTH*CHANNELS + x];
sum = mad(image_dataPtr[x * DILATION_X], kernel_dataPtr[x], sum);
}
}
image_dataPtr += input_width * DILATION_Y;
@ -205,18 +193,13 @@ __kernel void ConvolveBasic(
image_dataPtr += imageSize - input_width*KERNEL_HEIGHT*DILATION_Y;
}
for (int kern = 0; kern < ZPAR; kern++)
{
if (kernelNum + kern < OUTPUT_Z)
{
int offset = convolved_image_offset + (kernelNum+kern)*output_height*output_width + outputY*output_width + outputX;
int offset = convolved_image_offset + out_idx;
#if APPLY_BIAS
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern] + bias[biasIndex + kern], biasIndex + kern);
int biasIndex = bias_offset + outputZ;
ACTIVATION_FUNCTION(convolved_image, offset, sum + bias[biasIndex], biasIndex);
#else
ACTIVATION_FUNCTION(convolved_image, offset, sum[kern], kernelNum + kern);
ACTIVATION_FUNCTION(convolved_image, offset, sum, outputZ);
#endif
}
}
}
}

11
modules/features2d/src/orb.cpp
View File

@ -131,12 +131,17 @@ static void
HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
std::vector<KeyPoint>& pts, int blockSize, float harris_k)
{
CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
CV_CheckTypeEQ(img.type(), CV_8UC1, "");
CV_CheckGT(blockSize, 0, "");
CV_CheckLE(blockSize*blockSize, 2048, "");
size_t ptidx, ptsize = pts.size();
const uchar* ptr00 = img.ptr<uchar>();
int step = (int)(img.step/img.elemSize1());
size_t size_t_step = img.step;
CV_CheckLE(size_t_step * blockSize + blockSize + 1, (size_t)INT_MAX, ""); // ofs computation, step+1
int step = static_cast<int>(size_t_step);
int r = blockSize/2;
float scale = 1.f/((1 << 2) * blockSize * 255.f);
@ -154,7 +159,7 @@ HarrisResponses(const Mat& img, const std::vector<Rect>& layerinfo,
int y0 = cvRound(pts[ptidx].pt.y);
int z = pts[ptidx].octave;
const uchar* ptr0 = ptr00 + (y0 - r + layerinfo[z].y)*step + x0 - r + layerinfo[z].x;
const uchar* ptr0 = ptr00 + (y0 - r + layerinfo[z].y)*size_t_step + (x0 - r + layerinfo[z].x);
int a = 0, b = 0, c = 0;
for( int k = 0; k < blockSize*blockSize; k++ )

26
modules/features2d/test/test_orb.cpp
View File

@ -141,5 +141,31 @@ TEST(Features2D_ORB, regression_16197)
ASSERT_NO_THROW(orbPtr->detectAndCompute(img, noArray(), kps, fv));
}
// https://github.com/opencv/opencv-python/issues/537
BIGDATA_TEST(Features2D_ORB, regression_opencv_python_537) // memory usage: ~3 Gb
{
applyTestTag(
CV_TEST_TAG_LONG,
CV_TEST_TAG_DEBUG_VERYLONG,
CV_TEST_TAG_MEMORY_6GB
);
const int width = 25000;
const int height = 25000;
Mat img(Size(width, height), CV_8UC1, Scalar::all(0));
const int border = 23, num_lines = 23;
for (int i = 0; i < num_lines; i++)
{
cv::Point2i point1(border + i * 100, border + i * 100);
cv::Point2i point2(width - border - i * 100, height - border * i * 100);
cv::line(img, point1, point2, 255, 1, LINE_AA);
}
Ptr<ORB> orbPtr = ORB::create(31);
std::vector<KeyPoint> kps;
Mat fv;
ASSERT_NO_THROW(orbPtr->detectAndCompute(img, noArray(), kps, fv));
}
}} // namespace

8
modules/videoio/src/cap_gstreamer.cpp
View File

@ -860,7 +860,7 @@ bool GStreamerCapture::open(const String &filename_, const cv::VideoCaptureParam
}
else
{
CV_WARN("Error opening file: " << filename << " (" << err->message << ")");
CV_WARN("Error opening file: " << filename << " (" << (err ? err->message : "<unknown reason>") << ")");
return false;
}
}
@ -868,9 +868,9 @@ bool GStreamerCapture::open(const String &filename_, const cv::VideoCaptureParam
{
GSafePtr<GError> err;
uridecodebin.attach(gst_parse_launch(filename, err.getRef()));
if (err)
if (!uridecodebin)
{
CV_WARN("Error opening bin: " << err->message);
CV_WARN("Error opening bin: " << (err ? err->message : "<unknown reason>"));
return false;
}
manualpipeline = true;
@ -2073,7 +2073,7 @@ void handleMessage(GstElement * pipeline)
gst_message_parse_error(msg, err.getRef(), debug.getRef());
GSafePtr<gchar> name; name.attach(gst_element_get_name(GST_MESSAGE_SRC (msg)));
CV_WARN("Embedded video playback halted; module " << name.get() <<
" reported: " << err->message);
" reported: " << (err ? err->message : "<unknown reason>"));
CV_LOG_DEBUG(NULL, "GStreamer debug: " << debug.get());
gst_element_set_state(GST_ELEMENT(pipeline), GST_STATE_NULL);

4
platforms/scripts/valgrind.supp
View File

@ -51,7 +51,7 @@
OpenCV-TLS-getTlsStorage
Memcheck:Leak
...
fun:_ZN2cvL13getTlsStorageEv
fun:_ZN2cv*L13getTlsStorageEv
}
{
@ -159,7 +159,7 @@
OpenCV-DNN-getLayerFactoryMutex
Memcheck:Leak
...
fun:_ZN2cv3dnn*L20getLayerFactoryMutexEv
fun:_ZN2cv3dnn*20getLayerFactoryMutexEv
}
{